An Engineering Principle Used by Mother Nature: Use of Feedback for Robust Columnar Development

  • K. P. Unnikrishnan
  • H. S. Nine

Abstract

In spite of the inherent variability in parameters that govern development, the columnar structures in mammalian sensory systems are surprisingly robust. For example, the average width of ocular dominance columns in cats is about 400 μm, with very little variability from animal to animal. In engineering, the effect of appropriate feedback on stable, dynamical systems is to make them robust with respect to noise, including parameter variations. The question we ask (and answer) in this paper is, if during neural development, mother nature is cleverly using this engineering principle. Through computer simulations of a biologically plausible model we demonstrate that, during the development of ocular dominance columns, this is indeed the case. Transient neuron populations such as the subplate may play a major role in the initial formation of these feedback circuits. For cleverly using feedback, mother nature also gets a bonus: the synaptic computations in the circuits are completely local and hence independent of the time constants associated with the dendritic arbors of post-synaptic (layer 4) neurons that may still be growing!

Keywords

Lateral Geniculate Nucleus Dendritic Arbor Axonal Projection Mother Nature Feedback Pathway 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    J. A. Robson, J. Comp Neurol., 216, 89, 1983.PubMedCrossRefGoogle Scholar
  2. [2]
    A. Peters and B. R. Payne, Cereb. Cortex, 3, 69, 1993.PubMedCrossRefGoogle Scholar
  3. [3]
    K. L. Allendoerfer and C. J. Shatz, Ann Rev Neurosci, 17, 185, 1994.PubMedCrossRefGoogle Scholar
  4. [4]
    G. Meyer and R. Ferres-Torres, J. Comp. Neue.., 228, 226, 1984.CrossRefGoogle Scholar
  5. [5]
    E. M. Callaway and L. C. Katz, J. Neurosci., 12, 570, 1992.PubMedGoogle Scholar
  6. [6]
    E. M. Callawy, J. L. Lieber, and K.A. Reese Preprint, 1996.Google Scholar
  7. [7]
    H.S. Nine, PhD thesis, Univ. Michigan, 1995.Google Scholar
  8. [8]
    E. Harth and K. P. Unnikrishnan, Intl. J Psychophysiol., 3, 101, 1985.CrossRefGoogle Scholar
  9. [9]
    E. Harth, K.P. Unnikrishnan, and A.S. Pandya, Science, 237, 187, 1987.CrossRefGoogle Scholar
  10. [10] E. Harth, A. S. Pandya, and K. R. Unnikrishnan. Conc. Neurosci., 1, 53, 1990. [1 I]
    J. Janakiraman, and K.P. Unnikrishnan, Proceedings of CNS93, 215, 1993.Google Scholar
  11. [12]
    P.S. Sastry, S. Singh, and K.P. Unnikrishnan, Submitted, 1996.Google Scholar
  12. [13]
    K.P. Unnikrishnan, and K.P. Venugopal, Neural Comp., 6, 469, 1994.CrossRefGoogle Scholar
  13. [14]
    K.P. Unnikrishnan, and H.S. Nine, Submitted, 1996.Google Scholar
  14. [15]
    N.S. Sekar and K.P. Unnikrishnan, Abstr. Lrn. Mem. Mtg, CSH Lab., 50, 1992.Google Scholar
  15. [16]
    K.P. Unnikrishnan, and N.S. Sekar, Soc. Neurosci. Abstr. 19, 241, 1993.Google Scholar
  16. [17]
    R. Yuste, M.J. Gutnick, D. Saar, K. Delayne, and D.W. Tank, Neuron, 13, 23, 1994.PubMedCrossRefGoogle Scholar
  17. [18]
    N. Spruston, Y. Schiller, G. Stuart, and B. Sakmann, Science, 268, 297, 1995.PubMedCrossRefGoogle Scholar
  18. [19]
    P.A. Anderson, J. Olavarria, and R.C. Vansluyters, J. Neurosci., 8, 2183, 1988.PubMedGoogle Scholar
  19. [20]
    D. Mumford, in: M.A. Arbib, ed., The handbook of brain theory and neural networks (MIT Press), 1995.Google Scholar
  20. [21]
    C. Koch, Neuroscience, 23, 399, 1987.PubMedCrossRefGoogle Scholar
  21. [22]
    R. Sadja and L.H. Finkel, J. Cog. Neurosci. 7, 267, 1995.CrossRefGoogle Scholar
  22. [23]
    S. Grossberg, Am. Scientist., 83, 438, 1995.Google Scholar
  23. [24]
    A. M. Sillito, et al., Nature, 369, 479, 1994.PubMedCrossRefGoogle Scholar
  24. [25]
    J. Yan and N. Suga, Science, 273, 1100, 1996.PubMedCrossRefGoogle Scholar
  25. [26]
    K. S. Narendra and A. M. Annaswamy, Stable Adaptive Systems (Prentice Hall), 1989.Google Scholar
  26. [27]
    B. C. Kuo, Automatic control systems (Prentice Hall), pp. 6–11, 1987.Google Scholar
  27. [28]
    K.D. Miller, J.B. Keller, and M.P. Stryker, Science, 245, 605, 1989.PubMedCrossRefGoogle Scholar
  28. [29]
    T.H. Brown, et al., in Single Neuron Computation, T. McKenna, J. Davis, S. F. Zometter, eds., (Academic Press), 1992.Google Scholar
  29. [30]
    C. J. Shatz et. al., CSFI Symp. Quan. Biol., 40, 269, 1990.Google Scholar
  30. [31]
    A. Ghosh and C.J. Shatz, Science, 255, 1441, 1992.PubMedCrossRefGoogle Scholar
  31. [32]
    R.O.L. Wong, M. Meister, and C.J. Shatz, Neuron, 11, 923, 1993.PubMedCrossRefGoogle Scholar
  32. [33]
    A.L. Humphrey, et al., J Comp Neur, 233, 159, 1985.PubMedCrossRefGoogle Scholar
  33. [34]
    Y. Hata, et al., J Neurophy.siol, 69, 40, 1993.Google Scholar
  34. [35]
    S. Lowel, J Neurosci, 14, 7451, 1994.Google Scholar
  35. [36]
    Y.C. Diao, et al., Exp Brain Res, 79, 271, 1990.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1997

Authors and Affiliations

  • K. P. Unnikrishnan
    • 1
    • 2
  • H. S. Nine
    • 2
  1. 1.Computation and Neural Systems, 139-74California Institute of TechnologyPasadenaUSA
  2. 2.Computer Science, 480-106-285GM Research LabsWarrenUSA

Personalised recommendations